TST: Add binocular test file and expand unit tests

- ideally we should replace this test file with a smaller one that also has a calibration in it
- I added unit tests for the binocular file test, and a lot of other unit tests to hopefully catch more of our codebase

eyelinkio/tests/test_edf.py

@@ -5,6 +5,8 @@
 from eyelinkio.utils import _get_test_fnames, requires_edfapi
 
 fnames = _get_test_fnames()
+mne = pytest.importorskip('mne')
+panadas = pytest.importorskip('pandas')
 
 @requires_edfapi
 def test_read_raw():
@@ -17,58 +19,147 @@ def test_read_raw():
         # tests dtypes are parsed correctly that is double only
         assert edf_file['samples'].dtype == np.float64
 
-        if fname.name == "test_2_raw.edf":  # First test file has this property
+        if fname.name == "test_raw_binocular.edf":
+            # XXX: ideally we should get a binocular file with a calibration
+            assert edf_file["info"]["eye"] == "BINOCULAR"
+            assert len(edf_file["discrete"]["blinks"]) == 195
+
+        elif fname.name == "test_2_raw.edf":  # First test file has this property
             for kind in ['saccades', 'fixations', 'blinks']:
                 assert edf_file["discrete"][kind][0]['stime'] < 12.0
-        assert edf_file['times'][0] < 1.0
-        assert edf_file["info"]["eye"] == "LEFT_EYE"
-        assert edf_file["info"]["ps_units"] == "PUPIL_AREA"
 
-pytest.importorskip('pandas')
+        elif fname.name == "test_raw.edf":
+            want_point_x = np.array([960, 1600, 320, 960, 960])
+            want_point_y = np.array([540, 540, 540, 720, 360])
+            want_offsets = np.array([0.12, 0.40, 0.12, 0.65, 0.35])
+            want_diff_x = np.array([0.3, -10.1, -4.5, -24.3, -7.3])
+            want_diff_y = np.array([-4.4, 10.9, 0.1, -2.9, -10.9])
+
+            assert len(edf_file["info"]["calibrations"]) == 1
+            cal = edf_file["info"]["calibrations"][0]
+            validation = cal["validation"]
+
+            np.testing.assert_allclose(validation["point_x"], want_point_x)
+            np.testing.assert_allclose(validation["point_y"], want_point_y)
+            np.testing.assert_allclose(validation["offset"], want_offsets)
+            np.testing.assert_allclose(validation["diff_x"], want_diff_x)
+            np.testing.assert_allclose(validation["diff_y"], want_diff_y)
+            assert cal["eye"] == "left"
+            assert cal["model"] == "HV5"
+            np.testing.assert_equal(cal["onset"], 0.136)
+        else:
+            raise ValueError(f"Unexpected file: {fname}")
+
+        if fname.name == "test_raw.edf" or fname.name == "test_2_raw.edf":
+            # the following should be true for both monocular test files
+            assert edf_file['times'][0] < 1.0
+            assert edf_file["info"]["eye"] == "LEFT_EYE"
+            assert edf_file["info"]["ps_units"] == "PUPIL_AREA"
+
+
 def test_to_pandas():
     """Test converting EDF to pandas DataFrame."""
-    fname = _get_test_fnames()[1] # test_raw.edf
-    edf_file = read_edf(fname)
-    dfs = edf_file.to_pandas()
-    assert isinstance(dfs, dict)
-    np.testing.assert_equal(dfs["discrete"]["blinks"]["eye"].unique(), "LEFT_EYE")
-    assert dfs["discrete"]["messages"]["msg"][0] == "RECCFG CR 1000 2 1 L"
-
-pytest.importorskip('mne')
+    fnames = _get_test_fnames() # test_raw.edf
+    for fname in fnames:
+        edf_file = read_edf(fname)
+        dfs = edf_file.to_pandas()
+        assert isinstance(dfs, dict)
+
+        if fname.name == "test_raw.edf":
+            np.testing.assert_equal(
+                dfs["discrete"]["blinks"]["eye"].unique(), "LEFT_EYE"
+                )
+            assert dfs["discrete"]["messages"]["msg"][0] == "RECCFG CR 1000 2 1 L"
+
+            # calibration
+            assert len(dfs["calibrations"]) == 5
+            want_offset = np.array([0.12, 0.40, 0.12, 0.65, 0.35])
+            np.testing.assert_allclose(dfs["calibrations"]["offset"], want_offset)
+
+        elif fname.name == "test_2_raw.edf":
+            np.testing.assert_equal(
+                dfs["discrete"]["blinks"]["eye"].unique(), "LEFT_EYE"
+                )
+            assert dfs["discrete"]["messages"]["msg"][0] == "RECCFG CR 1000 2 1 L"
+
+        elif fname.name == "test_raw_binocular.edf":
+            want_eyes = ["LEFT_EYE", "RIGHT_EYE"]
+            got_eyes = dfs["discrete"]["blinks"]["eye"].unique()
+            np.testing.assert_equal(got_eyes, want_eyes)
+
+        else:
+            raise ValueError(f"Unexpected file: {fname}")
+
 def test_to_mne():
     """Test converting EDF to MNE."""
     import mne
 
-    fname = _get_test_fnames()[1] # test_raw.edf
-    edf_file = read_edf(fname)
-    raw, cals = edf_file.to_mne()
-    assert isinstance(raw, mne.io.RawArray)
-    assert raw.info["sfreq"] == edf_file["info"]["sfreq"]
-    tz = raw.info["meas_date"].tzinfo
-    assert raw.info["meas_date"] == edf_file["info"]["meas_date"].replace(tzinfo=tz)
-    # annotations
-    assert len(raw.annotations) == 148
-    assert raw.annotations[92]["description"] == "BAD_blink"
-    np.testing.assert_allclose(raw.annotations[92]["onset"], 11.298)
-    np.testing.assert_allclose(raw.annotations[92]["duration"], .089)
-    ch_names = ('xpos_left', 'ypos_left', 'ps_left')
-    np.testing.assert_equal(raw.annotations[92]["ch_names"], ch_names)
-    np.testing.assert_allclose(raw.annotations[0]["onset"], 0.0)
-    # calibration
-    # these values were taken from the ASCII file (ground truth)
-    point_x = np.array([960, 1600, 320, 960, 960])
-    point_y = np.array([540, 540, 540, 720, 360])
-    offsets = np.array([0.12, 0.40, 0.12, 0.65, 0.35])
-    diff_x = np.array([0.3, -10.1, -4.5, -24.3, -7.3])
-    diff_y = np.array([-4.4, 10.9, 0.1, -2.9, -10.9])
-    want_positions = np.array([point_x, point_y]).T
-    want_gaze = np.array([point_x + diff_x, point_y + diff_y]).T
-    want_avg_error = np.mean(offsets)
-    want_max_error = np.max(offsets)
-    cal = cals[0]
-    assert cal["eye"] == "left"
-    np.testing.assert_allclose(cal["positions"], want_positions)
-    np.testing.assert_allclose(cal["gaze"], want_gaze)
-    np.testing.assert_allclose(cal["offsets"], offsets)
-    np.testing.assert_allclose(cal["avg_error"], want_avg_error)
-    np.testing.assert_allclose(cal["max_error"], want_max_error)
+    fnames = _get_test_fnames()
+    for fname in fnames:
+        edf_file = read_edf(fname)
+        raw, cals = edf_file.to_mne()
+
+        if fname.name == "test_raw.edf":
+            assert isinstance(raw, mne.io.RawArray)
+            assert raw.info["sfreq"] == edf_file["info"]["sfreq"]
+
+            tz = raw.info["meas_date"].tzinfo
+            want_measdate = edf_file["info"]["meas_date"].replace(tzinfo=tz)
+            assert raw.info["meas_date"] == want_measdate
+
+            # annotations
+            assert len(raw.annotations) == 148
+            assert raw.annotations[92]["description"] == "BAD_blink"
+            np.testing.assert_allclose(raw.annotations[92]["onset"], 11.298)
+            np.testing.assert_allclose(raw.annotations[92]["duration"], .089)
+            ch_names = ('xpos_left', 'ypos_left', 'ps_left')
+            np.testing.assert_equal(raw.annotations[92]["ch_names"], ch_names)
+            np.testing.assert_allclose(raw.annotations[0]["onset"], 0.0)
+
+            # calibration
+            # these values were taken from the ASCII file (ground truth)
+            point_x = np.array([960, 1600, 320, 960, 960])
+            point_y = np.array([540, 540, 540, 720, 360])
+            offsets = np.array([0.12, 0.40, 0.12, 0.65, 0.35])
+            diff_x = np.array([0.3, -10.1, -4.5, -24.3, -7.3])
+            diff_y = np.array([-4.4, 10.9, 0.1, -2.9, -10.9])
+            want_positions = np.array([point_x, point_y]).T
+            want_gaze = np.array([point_x + diff_x, point_y + diff_y]).T
+            want_avg_error = np.mean(offsets)
+            want_max_error = np.max(offsets)
+            cal = cals[0]
+            assert cal["eye"] == "left"
+            np.testing.assert_allclose(cal["positions"], want_positions)
+            np.testing.assert_allclose(cal["gaze"], want_gaze)
+            np.testing.assert_allclose(cal["offsets"], offsets)
+            np.testing.assert_allclose(cal["avg_error"], want_avg_error)
+            np.testing.assert_allclose(cal["max_error"], want_max_error)
+
+        elif fname.name == "test_2_raw.edf":
+            assert isinstance(raw, mne.io.RawArray)
+            assert raw.info["sfreq"] == edf_file["info"]["sfreq"]
+
+            tz = raw.info["meas_date"].tzinfo
+            got_measdate = edf_file["info"]["meas_date"].replace(tzinfo=tz)
+            want_measdate = raw.info["meas_date"]
+            assert want_measdate == got_measdate
+
+            want_ch_names = ["xpos_left", "ypos_left", "ps_left"]
+            np.testing.assert_equal(raw.ch_names, want_ch_names)
+
+            want_coil = mne.io.constants.FIFF.FIFFV_COIL_EYETRACK_POS
+            want_kind =  mne.io.constants.FIFF.FIFFV_EYETRACK_CH
+            assert raw.info["chs"][0]["kind"] == want_kind
+            assert raw.info["chs"][0]["coil_type"] == want_coil
+            assert raw.info["chs"][0]["loc"][3] == -1
+            assert raw.info["chs"][0]["loc"][4] == -1
+            assert raw.info["chs"][1]["loc"][4] == 1
+
+        elif fname.name == "test_raw_binocular.edf":
+            want_chs = [f"{ch}_{eye}"
+                        for ch in ["xpos", "ypos", "ps"]
+                        for eye in ["left", "right"]
+                        ]
+            np.testing.assert_equal(raw.ch_names, want_chs)
+        else:
+            raise ValueError(f"Unexpected file: {fname}")